Top Machine Learning Jobs in Minneapolis, MN

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

Leads and mentors an AI/ML engineering team developing, deploying, and maintaining enterprise solutions. Sets technical strategy, architecture, engineering standards, MLOps practices, and software delivery processes. Partners with business, product, and architecture leaders to deliver AI-driven operational and customer experience solutions. Oversees resources, schedules, risks, governance, security, compliance, model performance, system availability, and team productivity.

Lead the design, development, deployment, and technical direction of responsible Voice AI and machine learning capabilities. Mentor AI/ML engineers, establish engineering standards, and build scalable speech, transcription, translation, and multimodal solutions. Partner with business teams on architecture, cost estimation, roadmaps, vendor evaluation, and production delivery. Apply ethical AI, HIPAA-compliant security, cloud-native engineering, and LLM technologies to improve healthcare workflows and patient-agent experiences.

Designs and deploys enterprise AI/ML solutions, scalable models, data pipelines, and MLOps architectures in cloud production environments. Responsibilities include model monitoring and optimization, responsible AI implementation, workflow automation, emerging technology evaluation, cross-functional architecture collaboration, code reviews, and mentoring. The role focuses on healthcare challenges and supports production-grade machine learning systems, including real-time inference and continuous integration and deployment.

Lead AI/ML strategy and implementation for enterprise Engagement Technology Operations. Drive AIOps, generative AI, intelligent automation, observability, predictive monitoring, anomaly detection, automated remediation, and self-healing capabilities. Oversee platform reliability, incident response, disaster recovery, governance, vendor relationships, and operational transformation. Partner with business and technology leaders to define roadmaps, measure outcomes, communicate risks, and improve scalability, resilience, efficiency, and cost optimization across critical platforms.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Manage a portfolio of advertisers on the Adflow commerce media platform, building relationships, monitoring budgets and campaign performance, presenting business reviews, coordinating onboarding, recommending optimization strategies, and identifying revenue-growth opportunities. The role serves as the primary client contact, collaborates with internal campaign, media operations, creative, and finance teams, and uses performance marketing expertise and analytics to drive advertiser success and long-term partnerships.

Manage a portfolio of advertisers on the Adflow commerce media platform. Responsibilities include client onboarding, campaign coordination, budget and revenue pacing, performance reporting, business reviews, strategic recommendations, optimization feedback, and identifying opportunities to grow advertising spend and channel distribution. The role serves as the primary client contact while collaborating with business development, campaign strategy, creative, media operations, and finance teams.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads responsible AI engineering for production-grade generative, agentic AI, and data science systems. Defines fairness, explainability, privacy, safety, governance, and human oversight controls across engineering and MLOps/LLMOps pipelines. Architectures multi-cloud AI platforms, establishes technical standards, manages AI risk, and leads large global onshore and offshore teams. Partners with clients on major AI programs, supports sales efforts, and develops responsible AI playbooks, training, and governance capabilities.

Leads AI/ML engineering teams and technical strategy for enterprise-scale healthcare and pharmacy solutions. Oversees development, deployment, governance, MLOps, security, and operations for generative AI, LLM, NLP, predictive analytics, and agentic AI capabilities. Partners with product, engineering, data science, and executives to align technology with business goals, evaluate emerging technologies, improve automation, and develop engineering talent.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Design, optimize, and deploy machine learning models for mobile, embedded, and edge devices. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, cross-platform inference, accelerator tuning, secure model updates, hybrid edge-cloud architectures, telemetry, benchmarking, privacy, and responsible AI. Collaborate with hardware, firmware, and product teams while maintaining technical documentation and evaluating emerging edge AI technologies.

Develop and implement data science, predictive modeling, machine learning, simulation, and data engineering solutions for laboratory automation and digital R&D products. Lead pilots from opportunity identification through adoption, collaborate with scientists and technology teams, improve laboratory data quality and usability, integrate models into workflows, and measure business impact. The role requires technical leadership across multidisciplinary teams and contributes to the long-term Digital Lab roadmap.

Develops and implements digital science capabilities across laboratories, including predictive modeling, simulation, machine learning, AI-enabled workflows, data-driven experimentation, and lab automation. Partners with scientists, product developers, modeling teams, and data specialists to convert R&D challenges into scalable solutions. Leads digital science pilots, improves laboratory data quality and adoption of digital tools, evaluates emerging technologies, measures business impact, and contributes to the Digital Lab roadmap.

Leads the enterprise analytics and AI strategy, overseeing business intelligence, advanced analytics, population health analytics, AI governance, platforms, pipelines, and model deployment. Partners with senior leaders to translate complex data into business decisions, improve healthcare affordability and quality, and deliver measurable outcomes. Builds and leads a high-performing analytics organization, manages its budget, establishes responsible AI practices, and communicates recommendations to executives and the Board.

Analyzes complex marketing, healthcare, and digital datasets to generate actionable insights, dashboards, campaign KPIs, ROI and attribution models. Develops predictive analytics, machine learning, AI applications, and customer segmentation models to improve HCP engagement and marketing performance. Supports data governance, accessibility, compliance, and responsible AI practices while partnering with marketing, sales, legal, compliance, and IT teams.